Update on "Introduce hydra framework with backwards compatibility"

[ghstack-poisoned]

Author: jackzhxng

backends/cadence/aot/tests/test_remove_ops_passes.py

@@ -13,8 +13,6 @@
 import executorch.backends.cadence.aot.ops_registrations  # noqa
 import torch
 import torch.nn as nn
-import torch.nn.functional as F
-from executorch.backends.cadence.aot import compiler
 from executorch.backends.cadence.aot.compiler import export_to_edge
 from executorch.backends.cadence.aot.fuse_ops import FuseQuantDequantToRequantizePass
 from executorch.backends.cadence.aot.graph_builder import GraphBuilder
@@ -53,16 +51,15 @@ class TestRemoveOpsPasses(unittest.TestCase):
     )
     @torch.no_grad()
     def test_remove_to_ops(self, shape: Tuple[int]):
-        class M(torch.nn.Module):
-            def forward(self, x: torch.Tensor):
-                return exir_ops.edge.aten.to(x, dtype=torch.float32)
-
-        model = M()
-        x = torch.randn(shape)
-        graph_module = export_to_edge(model, (x,)).exported_program().graph_module
-        p = RemoveToOpsPass()
-
-        graph_after_passes = cast(PassResult, p(graph_module)).graph_module
+        builder = GraphBuilder()
+        x = builder.placeholder("x", torch.randn(*shape, dtype=torch.float32))
+        x = builder.call_operator(
+            op=exir_ops.edge.aten.to.dtype,
+            args=(x, torch.float32),
+        )
+        builder.output([x])
+        original = builder.get_graph_module()
+        graph_after_passes = cast(PassResult, RemoveToOpsPass()(original)).graph_module
 
         self.assertEqual(
             count_node(graph_after_passes, exir_ops.edge.aten.to.dtype),
@@ -83,31 +80,24 @@ def forward(self, x: torch.Tensor):
     )
     @torch.no_grad()
     def test_remove_nop_add_op_pass(self, shape: Tuple[int]):
-        class FullX(torch.nn.Module):
-            def forward(self, t: torch.Tensor):
-                return torch.add(torch.full(shape, 0), t)
-
-        class FullY(torch.nn.Module):
-            def forward(self, t: torch.Tensor):
-                return torch.add(t, torch.full(shape, 0))
-
-        model = FullX()
-        t = torch.full(shape, 3)
-        graph_module = export_to_edge(model, (t,)).exported_program().graph_module
-
-        p = RemoveNopAddOpPass()
-
-        graph_after_passes = cast(PassResult, p(graph_module)).graph_module
-        self.assertEqual(
-            count_node(graph_after_passes, exir_ops.edge.aten.add.Tensor),
-            0,
-        )
-
-        model = FullY()
-        graph_module = export_to_edge(model, (t,)).exported_program().graph_module
-
-        graph_after_passes = cast(PassResult, p(graph_module)).graph_module
-
+        builder = GraphBuilder()
+        x = builder.placeholder("x", torch.randn(*shape, dtype=torch.float32))
+        zeros = builder.call_operator(
+            op=exir_ops.edge.aten.full.default, args=(shape, 0)
+        )
+        left_add = builder.call_operator(
+            op=exir_ops.edge.aten.add.Tensor,
+            args=(zeros, x),
+        )
+        right_add = builder.call_operator(
+            op=exir_ops.edge.aten.add.Tensor,
+            args=(left_add, zeros),
+        )
+        builder.output([right_add])
+        original = builder.get_graph_module()
+        graph_after_passes = cast(
+            PassResult, RemoveNopAddOpPass()(original)
+        ).graph_module
         self.assertEqual(
             count_node(graph_after_passes, exir_ops.edge.aten.add.Tensor),
             0,
@@ -122,31 +112,24 @@ def forward(self, t: torch.Tensor):
     )
     @torch.no_grad()
     def test_remove_nop_mul_op_pass(self, shape: Tuple[int]):
-        class FullX(torch.nn.Module):
-            def forward(self, t: torch.Tensor):
-                return torch.mul(torch.full(shape, 0), t)
-
-        class FullY(torch.nn.Module):
-            def forward(self, t: torch.Tensor):
-                return torch.mul(t, torch.full(shape, 0))
-
-        model = FullX()
-        t = torch.full(shape, 3)
-        graph_module = export_to_edge(model, (t,)).exported_program().graph_module
-
-        p = RemoveNopMulOpPass()
-
-        graph_after_passes = cast(PassResult, p(graph_module)).graph_module
-        self.assertEqual(
-            count_node(graph_after_passes, exir_ops.edge.aten.mul.Tensor),
-            0,
-        )
-
-        model = FullY()
-        graph_module = export_to_edge(model, (t,)).exported_program().graph_module
-
-        graph_after_passes = cast(PassResult, p(graph_module)).graph_module
-
+        builder = GraphBuilder()
+        x = builder.placeholder("x", torch.randn(*shape, dtype=torch.float32))
+        zeros = builder.call_operator(
+            op=exir_ops.edge.aten.full.default, args=(shape, 0)
+        )
+        left_mul = builder.call_operator(
+            op=exir_ops.edge.aten.mul.Tensor,
+            args=(zeros, x),
+        )
+        right_mul = builder.call_operator(
+            op=exir_ops.edge.aten.mul.Tensor,
+            args=(left_mul, zeros),
+        )
+        builder.output([right_mul])
+        original = builder.get_graph_module()
+        graph_after_passes = cast(
+            PassResult, RemoveNopMulOpPass()(original)
+        ).graph_module
         self.assertEqual(
             count_node(graph_after_passes, exir_ops.edge.aten.mul.Tensor),
             0,
@@ -159,18 +142,16 @@ def forward(self, t: torch.Tensor):
     )
     @torch.no_grad()
     def test_remove_alias_copy(self, shape: Tuple[int]):
-        class M(torch.nn.Module):
-            def forward(self, x: torch.Tensor):
-                return exir_ops.edge.aten.alias_copy(x)
-
-        model = M()
-        x = torch.randn(shape)
-        graph_module = export_to_edge(model, (x,)).exported_program().graph_module
-
-        p = RemoveAliasCopyOpPass()
-
-        graph_after_passes = cast(PassResult, p(graph_module)).graph_module
-
+        builder = GraphBuilder()
+        x = builder.placeholder("x", torch.randn(*shape, dtype=torch.float32))
+        alias = builder.call_operator(
+            op=exir_ops.edge.aten.alias_copy.default, args=(x,)
+        )
+        builder.output([alias])
+        original = builder.get_graph_module()
+        graph_after_passes = cast(
+            PassResult, RemoveAliasCopyOpPass()(original)
+        ).graph_module
         self.assertEqual(
             count_node(graph_after_passes, exir_ops.edge.aten.alias_copy.default),
             0,
@@ -183,19 +164,16 @@ def forward(self, x: torch.Tensor):
     )
     @torch.no_grad()
     def test_remove_detach_copy(self, shape: Tuple[int]):
-        # aten::detach is converted to aten::alias_copy after functionalization & decomposition.
-        class M(torch.nn.Module):
-            def forward(self, x: torch.Tensor):
-                return exir_ops.edge.aten.detach_copy(x)
-
-        model = M()
-        x = torch.randn(shape)
-        graph_module = export_to_edge(model, (x,)).exported_program().graph_module
-
-        p = RemoveDetachCopyPass()
-
-        graph_after_passes = cast(PassResult, p(graph_module)).graph_module
-
+        builder = GraphBuilder()
+        x = builder.placeholder("x", torch.randn(*shape, dtype=torch.float32))
+        detach = builder.call_operator(
+            op=exir_ops.edge.aten.detach_copy.default, args=(x,)
+        )
+        builder.output([detach])
+        original = builder.get_graph_module()
+        graph_after_passes = cast(
+            PassResult, RemoveDetachCopyPass()(original)
+        ).graph_module
         self.assertEqual(
             count_node(graph_after_passes, exir_ops.edge.aten.detach_copy.default),
             0,
@@ -210,95 +188,51 @@ def forward(self, x: torch.Tensor):
     def test_remove_zero_sized_constant_pad_nd(
         self, shape: Tuple[int], padding: Tuple[int]
     ):
-        # F.pad is converted to aten::constant_pad_nd after functionalization & decomposition.
-        class Padding(torch.nn.Module):
-            def __init__(self):
-                super().__init__()
-                self.padding = padding
-
-            def forward(self, x: torch.Tensor):
-                return F.pad(x, self.padding)
-
-        model = Padding()
-        x = torch.randn(shape)
-        graph_module = export_to_edge(model, (x,)).exported_program().graph_module
-
-        p = RemoveZeroSizedConstantPadNd()
-
-        graph_after_passes = cast(PassResult, p(graph_module)).graph_module
-
+        builder = GraphBuilder()
+        x = builder.placeholder("x", torch.randn(*shape, dtype=torch.float32))
+        pad = builder.call_operator(
+            op=exir_ops.edge.aten.constant_pad_nd.default, args=(x, padding)
+        )
+        builder.output([pad])
+        original = builder.get_graph_module()
+        graph_after_passes = cast(
+            PassResult, RemoveZeroSizedConstantPadNd()(original)
+        ).graph_module
         self.assertEqual(
             count_node(graph_after_passes, exir_ops.edge.aten.constant_pad_nd.default),
             0,
         )
 
     def test_remove_expand(self):
-        class Expand(torch.nn.Module):
-            def forward(self, x):
-                return torch.ops.aten.expand_copy(x, [2, 3, 5])
-
-        x = torch.ones(2, 3, 5)
-        p = RemoveNopExpandOpPass()
-        graph_module = export_to_edge(Expand(), (x,)).exported_program().graph_module
-        graph_module = p(graph_module).graph_module
-        # Assert that expand op is optimized away, since it is a nop
+        builder = GraphBuilder()
+        x = builder.placeholder("x", torch.randn([2, 3, 5], dtype=torch.float32))
+        expand = builder.call_operator(
+            op=exir_ops.edge.aten.expand_copy.default, args=(x, [2, 3, 5])
+        )
+        builder.output([expand])
+        original = builder.get_graph_module()
+        graph_after_passes = cast(
+            PassResult, RemoveNopExpandOpPass()(original)
+        ).graph_module
         self.assertEqual(
-            count_node(graph_module, exir_ops.edge.aten.expand_copy.default), 0
+            count_node(graph_after_passes, exir_ops.edge.aten.expand_copy.default), 0
         )
 
     def test_remove_zero_arg_cat(self):
-        class Cat(torch.nn.Module):
-            def forward(self, x, y):
-                return torch.ops.aten.cat((x, y), 0)
-
-        x = torch.ones(1, 0, 3, 5)
-        y = torch.ones(2, 0, 3, 5)
-        graph_module = (
-            compiler.export_to_cadence(Cat(), (x, y)).exported_program().graph_module
-        )
-        # Assert that cat op is optimized away, since it concatenates
-        # two zero-sized tensors
-        self.assertEqual(count_node(graph_module, exir_ops.edge.aten.cat.default), 0)
-
-    def test_remove_single_arg_cat(self):
-        class Cat(torch.nn.Module):
-            def forward(self, x, y):
-                z = torch.ones(0, 5)
-                # z is an empty tensor, and concatenation of x with z will
-                # be x. So we can safely eliminate the following cat op.
-                x1 = torch.ops.aten.cat((x, z))
-                x2 = torch.add(x1, 2.4, 3.1)
-                y1 = torch.add(y, 1, 2)
-                return torch.add(x2, y1)
-
-        x = torch.ones(3, 5)
-        y = torch.ones(3, 5)
-        graph_module = export_to_edge(Cat(), (x, y)).exported_program().graph_module
-        new_graph_module = RemoveZeroSizedCatArgsPass()(graph_module).graph_module
-        new_graph_module.graph.eliminate_dead_code()
-        # Assert that x1 is optimized away
-        self.assertEqual(count_node(new_graph_module, torch.ops.aten.cat.out), 0)
-
-    def test_remove_zero_sized_cat(self):
-        class Cat(torch.nn.Module):
-            def __init__(self, dim: int):
-                super().__init__()
-                self.dim = dim
-
-            def forward(self, tensors):
-                return torch.cat(tensors, self.dim)
-
-        shapes, dim, dtype, _max = [(1, 0, 3), (2, 0, 3)], 0, torch.float32, 127
-
-        in_tensors = [(torch.rand(shape) * _max).to(dtype=dtype) for shape in shapes]
-
-        model = Cat(dim)
-        graph_module = (
-            export_to_edge(model, (in_tensors,)).exported_program().graph_module
+        builder = GraphBuilder()
+        x = builder.placeholder("x", torch.randn([1, 0, 3, 5], dtype=torch.float32))
+        y = builder.placeholder("y", torch.randn([2, 0, 3, 5], dtype=torch.float32))
+        concat = builder.call_operator(
+            op=exir_ops.edge.aten.cat.default, args=([x, y], 0)
+        )
+        builder.output([concat])
+        original = builder.get_graph_module()
+        graph_after_passes = cast(
+            PassResult, RemoveZeroSizedCatArgsPass()(original)
+        ).graph_module
+        self.assertEqual(
+            count_node(graph_after_passes, exir_ops.edge.aten.cat.default), 0
         )
-        new_graph_module = RemoveZeroSizedCatArgsPass()(graph_module).graph_module
-        new_graph_module.graph.eliminate_dead_code()
-        self.assertEqual(count_node(new_graph_module, torch.ops.aten.cat.out), 0)
 
     def test_remove_clone(self):
         class Clone(torch.nn.Module):

backends/qualcomm/_passes/remove_redundancy.py

@@ -22,6 +22,7 @@ def __init__(self):
             exir_ops.edge.aten.clone.default: self._default_condition,
             torch.ops.aten.alias.default: self._default_condition,
             exir_ops.edge.aten.alias.default: self._default_condition,
+            exir_ops.edge.aten.alias_copy.default: self._default_condition,
             exir_ops.edge.aten.lift_fresh_copy.default: self._default_condition,
             # remove this target if '_skip_dim_order' is set to False
             exir_ops.edge.dim_order_ops._to_dim_order_copy.default: self._dim_order_op_condition,

backends/qualcomm/tests/models.py

@@ -10,6 +10,17 @@
 # module with related operator only
 
 
+# Ensure alias_copy is removed in remove_redundancy pass
+class Alias(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.relu = torch.nn.ReLU()
+
+    def forward(self, x):
+        alias_x = torch.ops.aten.alias.default(x)
+        return self.relu(alias_x)
+
+
 class And(torch.nn.Module):
     def __init__(self, pos, neg):
         super().__init__()

backends/qualcomm/tests/test_qnn_delegate.py

@@ -124,6 +124,11 @@ def test_qnn_backend_adaptive_avg_pool2d(self):
         sample_input = (torch.randn(1, 512, 7, 7),)
         self.lower_module_and_test_output(module, sample_input)
 
+    def test_qnn_backend_alias(self):
+        module = Alias()  # noqa: F405
+        sample_input = (torch.randn(1, 10),)
+        self.lower_module_and_test_output(module, sample_input)
+
     def test_qnn_backend_amax(self):
         modules = [AMax(dim=1, keepdim=False), AMax(dim=1, keepdim=True)]  # noqa: F405
         sample_input = (torch.randn(4, 4),)
@@ -1162,6 +1167,12 @@ def test_qnn_backend_adaptive_avg_pool2d(self):
         module = self.get_qdq_module(module, sample_input)
         self.lower_module_and_test_output(module, sample_input)
 
+    def test_qnn_backend_alias(self):
+        module = Alias()  # noqa: F405
+        sample_input = (torch.randn(1, 10),)
+        module = self.get_qdq_module(module, sample_input)
+        self.lower_module_and_test_output(module, sample_input)
+
     def test_qnn_backend_amax(self):
         modules = [AMax(dim=1, keepdim=False), AMax(dim=1, keepdim=True)]  # noqa: F405
         sample_input = (torch.randn(4, 4),)

codegen/test/TARGETS

@@ -0,0 +1,8 @@
+# Any targets that should be shared between fbcode and xplat must be defined in
+# targets.bzl. This file can contain xplat-only targets.
+
+load(":targets.bzl", "define_common_targets")
+
+oncall("executorch")
+
+define_common_targets()